Method for identifying a tooth region

ABSTRACT

A method for identifying tooth regions. The method includes generating a first threshold image from a first tooth image by selecting intensity data values higher than a first predetermined threshold value c 1;  generating a second threshold image from a second tooth image by selecting intensity data values higher than a second predetermined threshold value c 2;  generating a preliminary tooth regions image that defines at least a first tooth region from the intersection of the first and second threshold images; generating a reference binary image from the first image by selecting intensity data values higher than a third predetermined threshold value c 3,  wherein threshold value c 3  exceeds c 1;  and generating a refined tooth regions image from at least the first tooth region in the preliminary tooth regions image. The first tooth region is connected to objects in the reference binary image.

CROSS REFERENCE TO RELATED APPLICATIONS

Reference is made to U.S. Ser. No. ______ (Docket 94918) entitled A METHOD FOR LOCATING AN INTERPROXIMAL TOOTH REGION to Yan et al., filed on DATE, commonly assigned and incorporated herein by reference.

Reference is made to U.S. Ser. No. ______ (Docket 94919) entitled METHOD FOR EXTRACTING A CARIOUS LESION AREA to Yan et al., filed on DATE, commonly assigned and incorporated herein by reference.

FIELD OF THE INVENTION

The invention relates generally to the field of dental imaging, and in particular to a method for early detection of caries. More specifically, the invention relates to a method for identifying a tooth region in a tooth image captured using fluorescence and back-scattering of light.

BACKGROUND OF THE INVENTION

While there have been improvements in detection, treatment and prevention techniques, dental caries remains a prevalent condition affecting people of all age groups. If not properly and promptly treated, caries could lead to permanent tooth damage and even to loss of teeth.

Traditional methods for caries detection include visual examination and tactile probing with a sharp dental explorer device, often assisted by radiographic (x-ray) imaging. Detection using these methods can be somewhat subjective, varying in accuracy due to many factors, including practitioner expertise, location of the infected site, extent of infection, viewing conditions, accuracy of x-ray equipment and processing, and other factors. There are also hazards associated with conventional detection techniques, including the risk of damaging weakened teeth and spreading infection with tactile methods as well as exposure to x-ray radiation. By the time a caries condition is evident under visual and tactile examination, the disease is generally in an advanced stage, requiring a filling and, if not timely treated, possibly leading to tooth loss.

In response to the need for improved caries detection methods, there has been considerable interest in improved imaging techniques that do not employ x-rays. One method employs fluorescence wherein teeth are illuminated with high intensity blue light. This technique, sometimes termed quantitative light-induced fluorescence (QLF), operates on the principle that sound, healthy tooth enamel yields a higher intensity of fluorescence under excitation from some wavelengths than does de-mineralized enamel that has been damaged by caries infection. The correlation between mineral loss and loss of fluorescence for blue light excitation is then used to identify and assess carious areas of the tooth. A different relationship has been found for red light excitation, a region of the spectrum for which bacteria and bacterial by-products in carious regions absorb and fluoresce more pronouncedly than do healthy areas.

Applicants note some references related to optical detection of caries.

U.S. Pat. No. 4,515,476 (Ingmar) describes the use of a laser for providing excitation energy that generates fluorescence at some other wavelength for locating carious areas.

U.S. Pat. No. 6,231,338 (de Josselin de Jong et al.) describes an imaging apparatus for identifying dental caries using fluorescence detection.

U.S. Patent Application Publication No. 2004/0240716 (de Josselin de Jong et al.) describes methods for improved image analysis for images obtained from fluorescing tissue.

U.S. Pat. No. 4,479,499 (Alfano) describes a method for using transillumination to detect caries based on the translucent properties of tooth structure.

Among products for dental imaging using fluorescence behavior is the QLF Clinical System from Inspektor Research Systems BV, Amsterdam, The Netherlands. The Diagnodent Laser Caries Detection Aid from KaVo Dental Corporation, Lake Zurich, Ill., USA, detects caries activity monitoring the intensity of fluorescence of bacterial by-products under illumination from red light.

U.S. Patent Application Publication No. 2004/0202356 (Stookey et al.) describes mathematical processing of spectral changes in fluorescence in order to detect caries in different stages with improved accuracy. Acknowledging the difficulty of early detection when using spectral fluorescence measurements, the '2356 Stookey et al. disclosure describes approaches for enhancing the spectral values obtained, effecting a transformation of the spectral data that is adapted to the spectral response of the camera that obtains the fluorescent image.

While the described methods and apparatus are intended for non-invasive, non-ionizing imaging methods for caries detection, there is still room for improvement. One recognized drawback with existing techniques that employ fluorescence imaging relates to image contrast. The image provided by fluorescence generation techniques such as QLF can be difficult to assess due to relatively poor contrast between healthy and infected areas. As noted in the '2356 Stookey et al. disclosure, spectral and intensity changes for incipient caries can be very slight, making it difficult to differentiate non-diseased tooth surface irregularities from incipient caries.

Overall, it is recognized that, with fluorescence techniques, the image contrast that is obtained corresponds to the severity of the condition. Accurate identification of caries using these techniques often requires that the condition be at a more advanced stage, beyond incipient or early caries, because the difference in fluorescence between carious and sound tooth structure is very small for caries at an early stage. In such cases, detection accuracy using fluorescence techniques may not show marked improvement over conventional methods. Because of this shortcoming, the use of fluorescence effects appears to have some practical limits that prevent accurate diagnosis of incipient caries. As a result, a caries condition may continue undetected until it is more serious, requiring a filling, for example.

Detection of caries at very early stages is of particular interest for preventive dentistry. As noted earlier, conventional techniques generally fail to detect caries at a stage at which the condition can be reversed. As a general rule of thumb, incipient caries is a lesion that has not penetrated substantially into the tooth enamel. Where such a caries lesion is identified before it threatens the dentin portion of the tooth, remineralization can often be accomplished, reversing the early damage and preventing the need for a filling. More advanced caries, however, grows increasingly more difficult to treat, most often requiring some type of filling or other type of intervention.

To take advantage of opportunities for non-invasive dental techniques to forestall caries, it is necessary that caries be detected at the onset. In many cases, as is acknowledged in the '2356 Stookey et al. disclosure, this level of detection has been found to be difficult to achieve using existing fluorescence imaging techniques, such as QLF. As a result, early caries can continue undetected, so that by the time positive detection is obtained, the opportunity for reversal using low-cost preventive measures can be lost.

In commonly-assigned U.S. Patent Application Publication No. 2008/0056551, a method and apparatus that employs both the reflectance and fluorescence images of the tooth is used to detect caries. It takes advantage of the observed back-scattering, or reflectance, for incipient caries and in combination with fluorescence effects, to provide an improved dental imaging technique to detect caries. The technique, referred to as Fluorescence Imaging with Reflectance Enhancement (FIRE), helps to increase the contrast of images over that of earlier approaches, and also makes it possible to detect incipient caries at stages when preventive measures are likely to take effect. Advantageously, FIRE detection can be accurate at an earlier stage of caries infection than has been exhibited using existing fluorescence approaches that measure fluorescence alone. The application describes a downshifting method to generate the FIRE image.

Commonly-assigned copending PCT/CN2009/000078, entitled METHOD FOR DETECTION OF CARIES describes a morphological method for generating a FIRE image with reduced sensitivity to illumination variation.

Quantification of caries based on a digital image of a tooth such as a fluorescence image provides numerical information on the severity of lesion regions and can help dentists make and carry out treatment plans. It can be a useful tool in the longitudinal monitoring of caries for dentists to observe the evolution of each lesion area over time. U.S. Patent Application Publication No. 2004/0240716 has disclosed some methods for quantification of caries; however, the disclosed methods generally require manual extraction of lesion regions from sound tooth areas of the image by the user, and they are based on fluorescence-only images. Manual extraction of lesion regions from the image presents two problems. Firstly, the extraction process is slow, requiring the user to make many mouse clicks or to draw lines on the images to indicate the boundary of a lesion region. Secondly, manual extraction requires considerable caries diagnostic experience on the part of the user and is generally subjective. In addition, fluorescence-only images display incipient caries at relatively low contrast, further adding difficulty to the manual lesion extraction process. Therefore, in the disclosed methods, only compromised caries quantification results are achieved at best.

Commonly-assigned copending U.S. patent application Ser. No. 12/487,729 entitled METHOD FOR QUANTIFYING CARIES describes an improved method for quantifying caries. This method, however, partly relies on a key step of extracting a lesion area in a tooth image, which further partially relies on a key sub-step of identifying a tooth region.

There exist known methods for identifying a tooth region based on X-ray dental images using projection and active contour techniques, discussed by Jain et al., “Matching of dental x-ray images for human identification”, Pattern Recognition Vol. 37, pp. 1519-1532, 2004, and Chen et al., “Tooth contour extraction for matching dental radiographs”, ICPR 2004. These prior methods, however, are not applicable to a fluorescence image, reflectance image, or FIRE image because of significant differences in image content and characteristics. Thus, it can be seen that there is a need for a method for identifying a tooth region in a tooth image, particularly in a FIRE image or fluorescence image of a tooth.

SUMMARY OF THE INVENTION

An object of the present invention is to provide a method for identifying a tooth region in a digital image of a tooth, useful for extracting a carious lesion area and quantifying caries within that digital image.

Another object of the present invention is to provide a method for identifying a tooth region in a fluorescence image or FIRE image of a tooth.

A feature of the present invention is that tooth regions are automatically extracted from a FIRE image, the high contrast in the FIRE image providing improved sensitivity and accuracy for the identification of caries.

An advantage of the present invention is that tooth regions in tooth images are located automatically without user intervention, thus providing an efficient workflow in caries extraction, identification and monitoring.

These objects are given only by way of illustrative example, and such objects may be exemplary of one or more embodiments of the invention. Other desirable objectives and advantages inherently achieved by the disclosed invention may occur or become apparent to those skilled in the art. The invention is defined by the appended claims.

According to one aspect of the invention, there is provided a method for identifying tooth regions, executed at least in part on data processing hardware, the method comprising generating a first threshold image from a first image of the tooth by selecting intensity data values higher than a first predetermined threshold value c1; generating a second threshold image from a second image of the tooth by selecting intensity data values higher than a second predetermined threshold value c2; generating a preliminary tooth regions image from the intersection of the first and second threshold images; generating a reference binary image from the first image by selecting intensity data values higher than a third predetermined threshold value c3, wherein threshold value c3 exceeds threshold value c1; and generating a refined tooth regions image from regions that are in the preliminary tooth regions image and are connected to objects in the reference binary image.

According to another aspect of the invention, there is provided a method for identifying one or more tooth regions, executed at least in part on data processing hardware, the method comprising obtaining a fluorescence image of the tooth; obtaining a reflectance image of the tooth; combining image data for the fluorescence and reflectance images to obtain a combined image of the tooth, generating a complement image from a grayscale combined image of the tooth; generating an intermediate image by applying a morphological reconstruction operation to the complement image; generating a first threshold image by applying a thresholding operation to the intermediate image with a first upper threshold value and generating a first processed threshold image from the first threshold image; generating a second threshold image by applying the thresholding operation to the intermediate image with a second upper threshold value and generating a second processed threshold image from the second threshold image, the second upper threshold value being larger than the first upper threshold value; and identifying the tooth regions using the first and second processed threshold images.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features, and advantages of the invention will be apparent from the following more particular description of the embodiments of the invention, as illustrated in the accompanying drawings. The elements of the drawings are not necessarily to scale relative to each other.

FIG. 1 shows a method for quantifying caries comprising five steps using the present invention.

FIGS. 2A, 2B, 2C show illustratively a typical reflectance image, a fluorescence image, and a FIRE image, respectively.

FIG. 2D is a view showing the process for combining dental image data to generate a FIRE image.

FIG. 3A shows an embodiment of a digital image generation step.

FIG. 3B shows a tooth region in a FIRE image having a lesion area identified in an extraction step.

FIG. 3C shows an embodiment of a step for extracting a lesion area from sound tooth regions using the present invention.

FIG. 3C-1 shows the green channel of a reflectance image

FIG. 3C-2 shows the green channel of a fluorescence image.

FIG. 3C-3 shows a preliminary tooth regions image obtained from the green channel images shown in FIG. 3C-1 and FIG. 3C-2.

FIG. 3C-4 shows a reference binary tooth regions image obtained by thresholding the green channel of fluorescence image shown in FIG. 3C-2.

FIG. 3C-5 shows a refined tooth regions image obtained from the preliminary tooth regions image shown in FIG. 3C-3 and the reference binary image shown in FIG. 3C-4.

FIG. 3C-6 shows a processed refined tooth regions image obtained from the refined tooth regions image shown in FIG. 3C-5.

FIG. 3C-7 shows the green channel of a FIRE image.

FIG. 3C-8 shows an intermediate intensity image.

FIG. 3C-9 shows internal markers for teeth.

FIG. 3C-10 shows external markers for teeth.

FIG. 3C-11 shows the summation of the Sobel gradients Ifgrad and Iwgrad.

FIG. 3C-12 shows identified tooth regions according to one embodiment of the present invention.

FIG. 3C-13 shows a processed threshold image of teeth.

FIG. 3C-14 shows another processed threshold image of teeth.

FIG. 3C-15 shows the complement image of the processed threshold image shown in FIG. 3C-14.

FIG. 3C-16 shows the dilated image generated from the complement image shown in FIG. 3C-15.

FIG. 3C-17 shows identified tooth regions according to another embodiment of the present invention.

FIG. 3D shows a FIRE image with a dilated line in a sound tooth area, and a lesion segmentation border separating a sound tooth area and a lesion area after a sound region identification step.

FIG. 3E shows an embodiment of an intensity reconstruction step using a bilinear interpolation.

FIG. 4A shows a binary image of three teeth.

FIG. 4B shows a contour line formed from a fan of ray lines from the origin point.

FIG. 4C shows determined internal and external markers.

FIG. 4D is an illustration of the marker-controlled watershed result.

FIG. 4E is an illustration of interlines between adjacent teeth.

FIG. 5A shows a binary image of three teeth similar to FIG. 4A.

FIG. 5B shows a distance image Idist formed from a distance transformation on the image of FIG. 5A.

FIG. 5C shows seed points in seeded areas.

FIG. 5D shows internal and external markers.

FIG. 5E is an illustration of interlines after the marker-controlled watershed and distance transform processing.

FIG. 6A shows a method for quantification of caries using the present invention.

FIG. 6B shows another method for quantifying caries using the present invention.

DETAILED DESCRIPTION OF THE INVENTION

The following is a detailed description of the preferred embodiments of the invention, reference being made to the drawings in which the same reference numerals identify the same elements of structure in each of the several figures.

Reference is made to commonly-assigned copending U.S. patent application Ser. No. 12/487,729 filed on Jun. 19, 2009 and entitled METHOD FOR QUANTIFYING CARIES, by Liangliang Pan et al.

Reference is made to PCT/CN2009/000078, filed on Jan. 20, 2009, entitled METHOD FOR DETECTION OF CARIES, by Wei Wang et al.

Reference is made to U.S. Patent Application Publication No. 2008/0056551, published Mar. 6, 2008, entitled METHOD FOR DETECTION OF CARIES, by Wong et al.

Reference is made to U.S. Patent Application Publication No. 2008/0063998, published Mar. 13, 2008, entitled APPARATUS FOR CARIES DETECTION, by Liang et al.

Reference is made to U.S. Patent Application Publication No. 2008/0170764, published Jul. 17, 2008, entitled SYSTEM FOR EARLY DETECTION OF DENTAL CARIES, by Burns et al.

Reference is made to U.S. Patent Publication No. 2007/0099148, published on May 3, 2007, entitled METHOD AND APPARATUS FOR DETECTION OF CARIES, by Wong et al.

This invention includes calculation steps. Those skilled in the art will recognize that these calculation steps may be performed by data processing hardware that is provided with instructions for image data processing. Because such image manipulation systems are well known, the present description is directed more particularly to algorithms and systems that execute the method of the present invention. Other aspects of such algorithms and systems, and data processing hardware and/or software for producing and otherwise processing the image signals may be selected from such systems, algorithms, components and elements known in the art. Given the description as set forth in the following specification, software implementation lies within the ordinary skill of those versed in the programming arts.

The stored instructions of such a software program may be stored in a computer readable storage medium, which may comprise, for example: magnetic storage media such as a magnetic disk or magnetic tape; optical storage media such as an optical disc, optical tape, or machine readable bar code; solid state electronic storage devices such as random access memory (RAM), or read only memory (ROM); or any other physical device or medium employed to store a computer program. Using such software, the present invention can be utilized on a data processing hardware apparatus, such as a computer system or personal computer, or on an embedded system that employs a dedicated data processing component, such as a digital signal processing chip. Output resulting from executing the method of the present invention is provided as digital data for image display, for storage in an electronic memory, and, optionally, for use by other diagnostic image processing utilities.

In this disclosure, the word “intensity” is used to refer to light level, and is also broadly used to refer to the value of a pixel in a digital image.

Where they are used, the terms “first”, “second”, and so on, do not necessarily denote any ordinal or priority relation, but may be simply used to more clearly distinguish one element from another.

The term “water basin” as used herein is a term of art used to describe a structure that is identified and used in executing a marker-controlled watershed transformation in the imaging arts. The term “catchment basin” is sometimes used in the same way. References in this disclosure to “water basin” refer to this imaging arts construct.

Referring to FIG. 1, a method for quantifying caries, executed at least in part on data processing hardware such as computer hardware, comprises a step 110 of generating a digital image of a tooth, the image comprising actual intensity values for a region of pixels corresponding to the tooth, gum, and background; a step 120 of extracting a lesion area from sound tooth regions by identifying tooth regions, extracting suspicious lesion areas, and removing false positives; a step 130 of identifying a sound region that is adjacent to the extracted lesion area; a step 140 of reconstructing intensity values for tooth tissue within the lesion area according to values in the adjacent sound region; and a step 150 of quantifying the condition of the caries using the reconstructed intensity values and intensity values from the lesion area. Note that the phrase “extracting a lesion area,” as used throughout this application, means identifying at least one lesion area in a digital tooth image.

FIGS. 2A, 2B, and 2C show illustratively a typical reflectance image 167, a fluorescence image 168, and a FIRE image 169, respectively, of a tooth surface including a sound tooth area 164 and an early lesion area (or caries region) 162. Generally, in a reflectance image, such as a white light reflectance image, the intensity of early caries regions is higher than that of their surrounding sound areas. In contrast, in a fluorescence image, such as one obtained under blue excitation light, the intensity of caries regions is lower than that of their surrounding sound areas because of the fluorescence loss in caries regions. A FIRE image is obtained through subtracting regional maxima and dome regions of the reflectance image from the fluorescence image. As a result, the FIRE image has a similar appearance as a fluorescence image because both have lower intensity values in a lesion area than in a surrounding sound area. However, the FIRE image has higher contrast than a fluorescence image, making it potentially more sensitive in detecting caries. It should be noted that other images that are generated by combining image data for the fluorescence and reflectance images can also be used for substituting the FIRE image.

FIG. 2D corresponds to FIG. 5 of commonly-assigned copending U.S. Patent Application Publication No. 2008/0056551 (Wong et al.), entitled METHOD FOR DETECTION OF CARIES. This figure shows that a FIRE image 169 is formed by combining the fluorescence image 168 with the reflectance image 167 through a processing apparatus 180.

In the image processing field, there are many well known methods used to extract features from images, including but not limited to threshold, top-hat, and morphological grayscale reconstruction techniques (see Luc Vincent, “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms”, IEEE Transaction on Image Processing, Vol. 2, No. 2, pp. 176-201, 1993). However, not every technique is suitable for segmenting lesions from an image of a tooth. Teeth images have many characteristics that pose challenges for doing automatic lesion extraction. For example, a tooth image has no flat background (sound tooth areas are the background of the target caries), the caries have no fixed sizes and shapes, and surface contour and curvature of teeth cause uneven illumination, resulting in intensity variation across the tooth image. The present invention overcomes these difficulties by employing a combination of different image processing techniques that address the various problems specific to automatic processing of teeth images.

In the following, steps for quantifying caries are described in reference to FIGS. 3A through 5E. The methods for identifying a tooth region according to the present invention, essential for a method for quantifying caries and extracting a carious lesion region, are also described.

Step 110 of Generating a Digital Image of a Tooth

FIG. 3A shows one embodiment of step 110 of generating a digital image of a tooth, comprising steps of obtaining a fluorescence image, obtaining a reflectance image, and combining image data for the fluorescence and reflectance images to generate an image such as a FIRE image. Details of how the fluorescence and reflectance images are obtained are described in U.S. Patent Application Publication No. 2008/0063998, published Mar. 13, 2008, entitled APPARATUS FOR CARIES DETECTION, by Liang et al. According to this embodiment, the digital image of the tooth is a FIRE image 169, which is formed by combining the fluorescence image 168 with the reflectance image 167 through a processing apparatus 180 as shown in FIG. 2D.

The details of generating the FIRE image have been disclosed in the commonly-assigned copending PCT/CN2009/000078, entitled METHOD FOR DETECTION OF CARIES. Major steps of generating the FIRE image are as follows.

1. Obtaining a reflectance image, and then converting the reflectance image into a gray reflectance image with an intensity value of Iwgreen. The gray reflectance image can be the green channel of the reflectance image. This gray reflectance image is treated as a mask, and it has an intensity value of Imask=Iwgreen. In one example, the reflectance image is a white light reflectance image. The white light can be emitted from one or more white LEDs.

2. Generating a marker with an intensity value of Imarker according to the following formula,

Imarker=Imask−hdome,

where hdome, representing the height of a dome in the gray reflectance image, is a fixed value and is empirically selected based on the intensity values of a plurality of gray reflectance teeth images obtained. In one inventive example, hdome is 50.

3. Generating a reconstructed image having an intensity value of Ireconstructed through morphological grayscale reconstruction, which takes Imask and Imarker as input (see the Luc Vincent article, cited earlier).

4. Generating an image of regional maxima and dome regions of the gray reflectance image. This image, corresponding to the suspicious caries regions, has an intensity value

Ihdome=Imask−Ireconstructed.

5. Generating a FIRE image with an intensity value

I _(FIRE) =I _(Fluo) −Ihdome,

where I_(FIRE) and I_(Fluo) are the intensity values of the green channel of the generated FIRE image and the obtained fluorescence image, respectively. The generated FIRE image can be displayed as a color image by combining I_(FIRE) with the red and blue channels of the fluorescence image. In one example, the fluorescence image is one obtained under blue excitation light. The blue light can be emitted from one or more blue LEDs. The FIRE image is the digital image used for subsequent image processing steps.

Another embodiment of step 110 of generating a digital image of a tooth comprises a step of obtaining a fluorescence image. The fluorescence image is the digital image used for subsequent image processing steps.

Step 120 of Extracting a Lesion Area from Sound Tooth Regions

Generally, a digital image of a tooth can be classified into three groups of regions: 1) gum, 2) tooth, and 3) other background. Caries detection only needs to be performed inside tooth regions 165.

Referring to FIG. 3B, inside the tooth region 165 is a lesion area 162, a surrounding sound tooth area 164, and segmentation border 163 that separates the two areas. Methods for identifying tooth region 165, lesion area 162, surrounding sound tooth area 164, and segmentation border 163 are described below.

FIG. 3C shows an embodiment of step 120 for extracting a lesion area 162 from tooth regions 165 in a digital image of a tooth using the present invention. Step 120 is performed automatically without a need for user input. Specifically, step 120 includes sub-steps of identifying the tooth regions 165, extracting one or more suspicious lesion areas, and removing false positives. These sub-steps include details specific to tooth images, as discussed below.

Since some image processing work is done on a certain channel of a color image, for convenience, the following terms Iwred, Iwgreen, Iwblue, Ibred, Ibgreen, Ibblue, Ifred, Ifgreen, and Ifblue are used to represent the intensity values of the pixels in the red, green, and blue channels of the reflectance, fluorescence, and FIRE images, respectively. And in order to remove the impact of illumination level, intensity values of both reflectance and fluorescence images are adjusted to a range between 0 and 150, where 0 and 150 correspond to minimum and maximum intensity values.

Sub-Step of Identifying Tooth Regions 165 Method 1: Threshold Technique

As previously discussed, similar to the fluorescence image, the FIRE image has higher green intensity values within normal/sound tooth areas than in caries and other background areas. Consequently, an adapted threshold technique is preferably used on a fluorescence or FIRE image to separate the tooth region, which contains both normal/sound tooth areas and caries areas, from the gum and other background.

According to one embodiment, tooth regions 165 are identified from the digital tooth image as follows. In this embodiment and other embodiments throughout the disclosure, grayscale versions of both the fluorescence and reflectance images are used, the grayscale images being generated from one channel of their respective color images, such as the green channel, or from a mixing of the three channels using methods well known in the image processing art. For illustrative purposes, the embodiment is described below using the green channels of the fluorescence and reflectance images, Ibgreen and Iwgreen, respectively.

Threshold images are generated from Ibgreen (shown in FIG. 3C-1) and Iwgreen (shown in FIG. 3C-2) by selecting intensity values higher than some predetermined threshold values c1 and c2, for example, 10 and 30, respectively. Secondly, the intersection regions of the two threshold images are taken as the preliminary tooth regions image Iroi0, which is shown in FIG. 3C-3. Thirdly, a reference binary tooth regions image Irefroi (shown in FIG. 3C-4) is obtained by thresholding the image Ibgreen with a threshold value c3 higher than the one used in generating Iroi0, such as 30. And lastly, a refined tooth regions 165 image (shown in FIG. 3C-5), Iroi, is generated by choosing the regions that are in Iroi0 and connected to the objects in Irefroi.

To reduce the adverse impact of noise, optionally the refined tooth regions image Iroi can be further processed to form a processed refined image of tooth regions 165 as follows. In the refined tooth regions image Iroi, the regions with small areas, for example, an area with fewer than 20000 pixels for a 1280×1024 image, are removed. Subsequently, holes are filled to generate a processed tooth regions image shown in FIG. 3C-6, where regions having “0” value and surrounded by regions of “1” are replaced with “1”. This step of filling holes is also commonly referred to as a flood-fill operation.

The above four steps and the optional step increase the accuracy of selecting tooth regions 165 as compared to thresholding just the FIRE or fluorescence image. The refined tooth regions image or the processed refined tooth regions image is then used in the following sub-steps of extracting suspicious lesion areas and removing false positives.

In an alternative embodiment, thresholding technique is applied to the fluorescence or FIRE image to determine tooth regions 165. This embodiment helps to provide simpler and faster processing.

Method 2: Marker-Controlled Watershed-Based Method

According to another embodiment, the method for identifying tooth region 165 is based on marker-controlled watershed techniques. Firstly, the complement of Ifgreen image (shown in FIG. 3C-7) is calculated as the difference between the maximum value of Ifgreen image, max(Ifgreen), and Ifgreen from the following equation:

Ifgcomplement=max(Ifgreen)−Ifgreen,   Eq. (1.1)

where max means the function of extracting the maximal value.

Secondly, an intermediate intensity image I_(int erm) is obtained (shown in FIG. 3C-8) by applying a morphological reconstruction operation to the complement image Ifgcomplement, according to the following equation:

I _(int erm) =Ifgcomplement−imreconstruct(Ifgcomplement−h,Ifgcomplement),   Eq. (1.2)

where imreconstruct is the operation of morphological reconstruction, and h is an experimentally determined parameter. In one example, h=50.

Thirdly, internal and external markers for teeth are determined. In one example, with a first upper threshold value T11, such as 5, thresholding operation is first applied to the intermediate intensity image I_(int erm) to form a first threshold image Ithres11. The first threshold image Ithres11 is a binary image, in which “0” (or “1”) means that the corresponding pixel value of I_(int erm) is higher (or lower) than the first upper threshold value T11. In the first threshold image Ithres11, the regions with small areas, for example, an area with fewer than 20000 pixels for a 1280×1024 image, are removed. Independently, holes that are created from the thresholding operation are filled to generate a first processed threshold image Ithres11proc, where regions having “0” value and surrounded by regions of “1” are replaced with “1”. After that, the internal marker (shown in FIG. 3C-9) is determined by eroding the first processed threshold image Ithres11proc with a disk shaped structure element, the radius of which is 20 pixels in one example.

Similarly, the external marker can be obtained using a second upper threshold value T12 which is greater than the first upper threshold value T11, namely, T12>T11. In one example, T12=40. Thresholding operation is applied to I_(int erm) with T12 to form a second threshold image Ithres12, which is also a binary image. Likewise, in the second threshold image Ithres12, “0” (or “1”) means that the corresponding pixel value of I_(int erm) is higher (or lower) than the second upper threshold value T12. Then, the regions of Ithres12 with small areas are removed, and the holes resulting from the thresholding operation are filled to generate a second processed threshold image Ithres12proc, in a manner similar to that described in the preceding paragraph. The second processed threshold image Ithres12proc is then dilated with a disk shaped structure element to form a dilated image, the complement of which forms the external marker shown in FIG. 3C-10.

Next, the Sobel gradient images Ifgrad and Iwgrad are calculated from Ifgreen, the green channel of the FIRE image, and Iwgreen, the green channel of the reflectance image, respectively. By using the internal and external markers and applying the marker-controlled watershed transform to the summation of Ifgrad and Iwgrad (that is, Ifgrad+Iwgrad), shown in FIG. 3C-11, the contour of the tooth regions is thus obtained. Tooth regions as determined by this method are shown in FIG. 3C-12.

Method 3: Morphological Reconstruction-Based Method

According to another embodiment, the method for identifying a tooth region 165 is based on a morphological reconstruction technique. Firstly, using the same techniques as discussed in Method 2, the complement Ifgcomplement of Ifgreen image is calculated using Eq. (1.1), and an intermediate intensity image I_(int erm), the same as the one shown in FIG. 3C-8, is then obtained by applying a morphological reconstruction operation to the complement image Ifgcomplement, according to Eq. (1.2).

Secondly, two threshold images for teeth are determined. In one example, with a first upper threshold value T21, such as 5, thresholding operation is first applied to I_(int erm) to form a first threshold image Ithres21. The first threshold image Ithres21 is a binary image, in which “0” (or “1”) means that the corresponding pixel value of I_(int erm) is higher (or lower) than the first threshold value T21. In the first threshold image Ithres21, the regions with small areas, for example, an area with fewer than 20000 pixels for a 1280×1024 image, are removed. Independently, holes that are created from the thresholding operation are filled to generate a first processed threshold image Ithres21proc, where regions having “0” value and surrounded by regions of “1” are replaced with “1”. The first processed threshold image Ithres21proc obtained is shown in FIG. 3C-13.

Similarly, a second threshold image Ithres22 is first obtained by applying a thresholding operation using a second upper threshold value T22 that is larger than T21. In one example, T22=40. Then, in the second threshold image Ithres22, the regions with small areas, for example, an area with fewer than 20000 pixels for a 1280×1024 image, are removed. Independently, holes that are created from the thresholding operation are filled to generate a second processed threshold image Ithres22proc, where regions having “0” value and surrounded by regions of “1” are replaced with “1”. The second processed threshold image Ithres22proc obtained is shown in FIG. 3C-14.

Thirdly, the complement image of the second processed threshold image, Ithres22proc′, shown in FIG. 3C-15, is dilated iteratively with a 3×3 structure element of all 1's. When any point of the “1” regions in the dilated image Idilated, shown in FIG. 3C-16, touches the “1” regions of the first processed threshold image Ithres21proc, the iteration will be stopped. Then the complement of the dilated image is formed. Finally, the “1” regions of the complement image are taken to be the identified tooth regions, as shown in FIG. 3C-17.

Sub-Step of Extracting a Suspicious Lesion Area

In a FIRE image (Ifgreen), there is a definite morphological characteristic for caries, that is, the intensity of region of caries 162 is lower than that of the surrounding sound tooth area 164. This characteristic is used to detect and segment the suspicious caries areas based on mathematical morphology theory.

In one embodiment, a marker-controlled watershed based method is adapted to detect and segment the suspicious caries areas. The key to this method is to determine internal and external markers for the target objects. According to one example, the internal markers are determined with the morphological grayscale reconstruction technique. The same technique has also been used for generating a FIRE image as discussed above.

To determine internal markers with the morphological gray-scale reconstruction method, the regional basins Ihbasin are first detected; they correspond to the target regions of caries because they have lower intensity than surrounding sound areas. Then, the internal markers are obtained by thresholding Ihbasin with a fixed value, for example, 50. Note that the fixed value can be adjusted according to detection sensitivity requirement. The internal markers are the regions inside which the intensities of Ihbasin are higher than the given threshold value.

To obtain the external markers, a binary image is first formed from the internal markers, wherein the pixel value of the binary image is 1 for a pixel inside internal markers and is 0 otherwise. Then a distance transformation (DT), mapping each image pixel onto its shortest distance to the target objects, is applied to the binary image to generate a DT image (see “Sequential operations in digital picture processing”, J. ACM. 13, 1966, by Rosenfeld, A. and Pfaltz, J. and “2D Euclidean distance transform algorithms: a comparative survey”, ACM computing surveys 40, 2008, by Ricardo Fabbri, Luciano Da F. Costa, Julio C. Torelli and Odemir M. Bruno). The ridge lines that are composed of the pixels with local maximal values in the DT image and located between the internal markers are taken as the external markers.

Next, the gradient image of Ifgreen is calculated with the Sobel operator. The Sobel operator is an image processing function well known to those skilled in the image processing/pattern recognition art; a description of it can be found in Pattern Classification and Scene Analysis, Duda, R. and Hart, P., John Wiley and Sons, 1973, pp. 271-272.

With the internal and external markers and the gradient image identified or determined, marker-controlled watershed transformation is then applied to generate a contour of the target regions of caries 162 directly. A description of the marker-controlled watershed transformation can be found in “Morphological grayscale reconstruction in image analysis: applications and efficient algorithms”, IEEE Transactions on Image Processing, Vol. 2, pp. 176-201, 1993, by Luc Vincent.

In another embodiment, a method based on a morphological bottom-hat operation along with the multi-resolution and surface reconstruction is adapted to detect and segment the suspicious caries areas. According to this embodiment, a bottom-hat operation is first applied to Ifgreen to produce an original bottom-hat image with an intensity value of Ibothat. Then a multi-resolution strategy is adapted to enable detection of caries with different sizes. According to this strategy, the original bottom-hat image is down-sampled to form one or more reduced-resolution bottom-hat images, such as 2×-down sampled image and 4×-down sampled image. Given a 2-dimensional shaped structure element with a fixed size, for example, a disk with a radius of 10 pixels, the morphological bottom hat processing is then applied to the images with different resolutions (that is, to original bottom-hat image, to 2×-down sampled bottom-hat image, to 4×-down sampled bottom-hat image, etc.). Note that the 2-dimensional structure element can take other shapes. The size of the structure element, for example, the radius of the disk, can be adjusted according to the image resolution or the size of the target objects. For each of the obtained multi-resolution bottom-hat images, according to the statistic of the intensity value inside the corresponding tooth regions, a threshold value Ithres is calculated as

Ithres=Imean+w*Istd,

where w is the weighting parameter determined experimentally, and Imean and Istd are the mean and standard deviation of intensity values, respectively. Applying a threshold operation to each of the multi-resolution bottom-hat images, a binary image is obtained, inside which the regions with a nonzero value are the initial suspicious caries areas in the image with corresponding resolution. After interpolating each of the binary images back to the original resolution to produce interpolated images, the union of all the interpolated images is taken as the initial suspicious lesion areas.

Since unable to use an infinite number of resolutions, and the size and shape of the structure elements are not the same as those of the target regions of caries 162, the initial suspicious caries areas are usually not the optimal results.

However, by using a small value of the weighting parameter w, the target caries areas can be included inside the initial suspicious caries areas with high confidence. In one example, the weighting parameter w is 1.0, 0.5, and 0 for the original, 2×-down sampled, and 4×-down sampled images, respectively. Certainly, the weighting parameter w can be adjusted according to practical requirements.

The normal intensity values (i.e., intensity values of the areas prior to the development of caries) inside the initial suspicious caries areas can be further estimated according to those outside the initial suspicious caries areas. According to one example, the intensity estimation is a surface reconstruction processing, generating Ireconstructed, where intensity is taken as a topological surface. Subtracting the original image Ifgreen from the reconstructed image Ireconstructed, a difference image Idiff is obtained. Because the intensity values inside the caries areas are lower than those of the normal/sound tooth areas, and the change between parts inside the normal or sound tooth areas is not as much as that between the caries and the normal/sound tooth areas, the regions with larger change in intensity values (for example, >7, which can be adjusted according to the required detection sensitivity) are taken as the refined suspicious caries areas.

While the morphological grayscale reconstruction technique could also be used to detect regional maxima-dome of a certain height or regional minima-basin of a certain depth in a grayscale image, it is not as suitable as the embodiments discussed above to extract caries lesion in teeth image. This is because different caries areas have different contrast with respect to their surrounding areas. Thus, different regional extrema heights or depths are needed to suit different images or different caries infections. After all, the height or depth is still a global parameter. Additionally, the morphological grayscale reconstruction is more difficult to be implemented and is slower than the morphological bottom-hat method.

While a conventional top/bottom hat method might also be considered for use to detect regional maxima dome or minima basin regions, the method also is unsuitable for extracting a caries lesion because it is difficult to determine the size of the structure element. This is unlike the morphological bottom-hat method discussed in this application, which when used along with the multi-resolution and surface reconstruction techniques, successfully overcomes the problem of determining the size of the structure element.

Sub-Step of Removing False Positives

Based on experimental results, most occurrences of false positives can be grouped into two categories: (1) areas having low contrast (typically lower than 7, though it can be adjusted according to the practical application) compared to the surrounding areas, and (2) areas between the proximal surfaces of adjacent teeth (hereafter referred to as interproximal regions).

Low contrast false positives are removed by calculating the intensity contrast between suspicious area and its surrounding area.

Interproximal false positives are removed according to the morphological features of the suspicious caries located inside or connected to the interproximal regions. To do this, the interproximal region is first identified.

A detailed description of how interproximal regions are located in teeth images according to the present invention is given below.

For adjacent teeth that are well separated, the interproximal regions contain spaces that are part of the background. This first kind of interproximal region having clear demarcation of the adjacent teeth is located as follows. Firstly, a distance transformation is applied to the binary image of tooth regions, and the pixel with the largest distance measured from the boundaries of the identified tooth regions in the binary image is located. Note that “largest distance” can be easily determined using any of a number of known algorithms. In the described embodiment, the pixel lying within a tooth region that has the largest distance to the boundary of the tooth region is most preferably chosen. However, any pixel with distance substantially close to the largest distance (for example, preferably within 90% of the largest distance, less preferably within at least 80% of the largest distance) may also be used, as empirically determined to yield satisfactory results. Accordingly, throughout this application, “the pixel with the largest distance” can be construed as “the pixel with distance substantially close to the largest distance” in the manner just described. Secondly, the identified tooth region that is connected to the located pixel is assigned as one object, and the other identified tooth regions are assigned as another object. And thirdly, the pixels in the background having substantially the same distance to each of the two objects are then defined to be the interproximal regions. “The same distance” in this application means that for two pixels, their respective distances to the same feature differ by no more than a small number of pixels, preferably by no more than from 1 to 5 pixels, less preferably by no more than about 10 pixels. This distance tolerance has been empirically determined to yield satisfactory results. Accordingly, throughout this application, the term “same distance” can be construed as “substantially same distance” for pixels in the manner just described.

For adjacent teeth that are adjoining or very close to each other, the interproximal regions do not contain a clear demarcation of the adjacent teeth. Different image processing approaches have to be taken to identify this second kind of interproximal region in tooth images. In the first inventive example, referring to FIGS. 4A through 4E, the second kind of interproximal regions are located in four steps with marker-controlled watershed transformation and distance transformation in the region connected to the pixel with the largest distance.

FIG. 4A shows a binary image of a target tooth 165 a and two neighboring teeth 165 b and 165 c. The light areas represent the teeth, while the dark areas represent background of the teeth. The light and dark areas are separated by a boundary. The origin point 200 is defined as the pixel with the maximal distance to the boundaries of the teeth, though any point near the center of the target tooth 165 a can also be chosen as the origin point. The origin point can also be determined with other methods according to practical applications. For example, if the tooth located at the center of the image is chosen as the target tooth, the local maxima point closest to the image center can be selected as the origin point. In the first step as shown in FIG. 4B, a fan of ray lines 210 are cast from the origin point 200 in every direction between 0° and 360°. Subsequently a contour line 202 is formed or defined from points at which each ray line 210 first encounters the boundary between the light and dark areas.

In the second step, internal and external markers are identified or determined as follows. As shown in FIG. 4C, internal makers are determined from a certain circular area 222 around the origin point 200 and the gray areas 220 a, 220 b, 220 c, 220 d. According to one example of the present invention, the radius of the circular area 222 is chosen as ¾ times of the maximal distance, the distance of the origin point 200 to the tooth boundaries. The gray areas 220 a, 220 b, 220 c, 220 d are obtained by subtracting the area enclosed by the contour line 202 from the tooth areas 165 a, 165 b, 165 c, which have been determined by the boundary between the light and dark areas in reference to FIG. 4A. The outer light areas 224, corresponding to the dark areas of FIG. 4A, are taken as the external markers.

In the third step, a marker-controlled watershed transformation is applied to a gradient image of a grayscale FIRE image with the above determined internal and external markers. In one embodiment, the grayscale FIRE image is generated from the green channel of the FIRE image, Ifgreen. In alternative embodiments, the grayscale FIRE image can be generated from a mixing of the three channels using methods well known in the image processing art. This transformation results in a water basin 170 connected to the internal marker that corresponds to the circular area 222 of FIG. 4C, and water basins 172 a, 172 b, 172 c, 172 d connected to the internal markers that correspond to the gray areas 220 a, 220 b, 220 c and 220 d of FIG. 4C, respectively. This transformation also results in watershed lines 173 a, 173 b. Watershed line 173 a separates water basins 172 a from 172 b, while watershed line 173 b separates water basins 172 c from 172 d. As noted earlier, the term “water basin”, also referred to as catchment basin, is a term of the marker-controlled watershed transformation art in imaging, known to a person skilled in the art.

In the fourth step, the pixels having the same distance to the two groups of basins are then taken to be the second kind of interproximal regions. FIG. 4E shows parts of the interlines 176, indicating locations of the interproximal regions that are identified. Interlines 176 are obtained by marker-controlled watershed transformation and distance transformation. Region 174 a is obtained from water basin 170. Region 174 b is obtained from a combination of water basins 172 a and 172 b. A region 174 c is obtained from a combination of water basins 172 c and 172 d.

In the second inventive example, referring now to FIGS. 5A through 5E, the second kind of interproximal regions that have no clear demarcation are located in four steps with a different adaptation of marker-controlled watershed transformation and distance transformation in the region connected to the pixel with the largest distance. Although sharing similar third and fourth steps, this second inventive example differs from the first inventive example in the first two steps.

Similar to FIG. 4A, FIG. 5A shows a binary image of a target tooth 165 a and two neighboring teeth 165 b and 165 c. The light areas represent the teeth, while the dark areas represent background of the teeth.

In the first step as shown in FIG. 5B, a distance transformation is applied to the image of FIG. 5A and results in a distance image Idist. Generally, the pixel value in the distance image represents the closest distance of that pixel to the background of the teeth.

In the second step shown in FIG. 5C and FIG. 5D, the internal markers 230 a, 230 b, 230 c and external marker 232 are determined as follows.

With Idist as the mask and Idist−dhome as the marker, using morphological grayscale reconstruction, a reconstructed image Idrecon can be obtained. Then Iseeds can be determined according to the following equation:

Iseeds=(Idrecon>Tdrecon)∩(Idist>Tdist),

where Tdrecon and Tdist are two threshold values (for example, Tdrecon=5, and Tdist=10), respectively. The symbol (Idrecon>Tdrecon) refers to the area in which the pixel values of Idrecon are greater than Tdrecon, and the symbol (Idist>Tdist) refers to the area in which the pixel values of Idist are greater than Tdrecon. The symbol ∩ is the intersection operator, familiar to those skilled in set theory.

Seeded regions 230 a, 230 b, 230 c obtained from Iseeds are shown in FIG. 5C. In each seeded region, according to the distance image Idist in FIG. 5B, a seed point is identified as the pixel with maximal distance. For example, seed points 234 a, 234 b, and 234 c are the pixels having maximal distance in seeded areas 230 a, 230 b, and 230 c, respectively. Taking the seed point as the origin point and ¾ times of its distance as the radius, for each seeded region, a circular region is created as an internal marker corresponding to the seed point. Specifically, circular internal markers 236 a, 236 b, and 236 c are created from seed points 234 a, 234 b, and 234 c, respectively, as shown in FIG. 5D. The background regions of the teeth are used as the external markers 232 a, 232 b.

Similar to the third step of the first inventive example (in reference to FIGS. 4A through 4E), in the third step, as shown in FIG. 5E, marker-controlled watershed transformation is applied to the gradient image of a grayscale FIRE image with the above determined internal markers 236 a, 236 b, and 236 c and external markers 232 a, 232 b, and water basin regions 238 a, 238 b, 238 c for internal markers 236 a, 236 b, and 236 c are obtained, respectively. Finally, in the fourth step, again similar to the fourth step of the first inventive example, interlines 240 a, 240 b are located as the pixels having the same distance to two neighboring water basin regions.

After the interproximal regions are located, the suspicious caries areas connected to the interproximal regions are then identified. Because some true caries are also located in these regions, not all the suspicious caries areas connected to the interproximal regions should be removed. A true caries often appears as a “grayscale hole”, which is an area of dark pixels surrounded by lighter pixels in the grayscale image. Thus, the “grayscale hole” characteristic is used to test which of the suspicious caries areas are true caries and should be retained, while the other suspicious areas connected to the interproximal regions are removed as false positives.

After the false positives are removed, the remaining suspicious caries areas are the extracted regions of caries 162. When displayed, these areas may be outlined or highlighted with false colors in a displayed FIRE, fluorescence, or reflectance image of the teeth to aid caries screening or diagnosis. They are also used for caries quantification analysis, in the steps described below.

Step 130 of Finding a Sound Tooth Region Adjacent to the Extracted Lesion Area

Referring back to FIG. 3D, step 130 of identifying a sound tooth region adjacent to the extracted lesion area is performed by expanding the suspicious lesion areas 162 outward to dilated line 166 with morphological dilation, an operation well known in the image processing art. This step is performed automatically without a need for user input. This step and steps 140 and 150 are preferably performed on the fluorescence image, for reasons explained below. The areas surrounding the expanded suspicious lesion areas are taken as the normal/sound areas, and the values of the pixels making up the dilated line 166 are taken as the intensity values of the surrounding normal/sound areas. The algorithmic implementation of the morphological dilation step is similar to that presented in FIG. 3 of commonly assigned co-pending U.S. Patent Application Publication No. 2008/0170764. This step reduces errors even if there are possible detection errors in the detected suspicious caries regions and in the non-significant intensity changes in normal/sound tooth areas.

Step 140 of Reconstructing Intensity Values for Tooth Tissue within the Lesion Area

For assessing the severity of the extracted lesions and for monitoring the development of the identified lesions over time, it is helpful to have an estimate of the normal intensity values of the suspicious caries regions before the development of caries. This can be performed through various approaches based on the intensity values of the surrounding normal/sound areas found in Step 130.

In one embodiment, after the surrounding sound area is identified, the reconstructed intensity value for tooth tissue within the lesion area can be obtained using a bilinear interpolation technique according to values in the adjacent sound region as described below.

FIG. 3E shows an exploded view of a region of interest 161 shown in FIG. 3D. For each pixel P in the lesion area R 162, there are four pixels on the dilated line 166 in the sound area that are to the left, right, top, and bottom of P, named P_(L), P_(R), P_(T), P_(B), respectively. The estimation of the reconstructed intensity value I_(r) at P can be calculated using a bilinear interpolation, for which the formulae are shown below.

$I_{H} = \frac{{I_{L} \cdot x_{2}} + {I_{R} \cdot x_{1}}}{x_{2} + x_{1}}$ $I_{V} = \frac{{I_{T} \cdot y_{2}} + {I_{B} \cdot y_{1}}}{y_{2} + y_{1}}$ $I_{r} = \frac{I_{H} + I_{V}}{2}$

Bilinear interpolation is carried out in this way for every pixel in the region of caries 162 to reconstruct the normal intensity values for the whole region.

As an alternative embodiment, after the surrounding sound area is identified, the reconstructed intensity value for tooth tissue within the lesion area can be obtained using a surface fitting technique such as a two-dimensional spline, or Bèzier fit.

Another alternative embodiment for reconstructing intensity value for tooth tissue within the lesion area is to smoothly interpolate inward from the pixel's values on the boundaries of the expanded suspicious caries areas by solving Laplace's equation. This embodiment is an adaptation of a common image processing technique (such as what has been implemented in the familiar Matlab software function “roifill” in its image processing toolbox), and results in more accurate estimation.

Step 150 of Quantifying the Condition of the Caries

As discussed above, quantitative information on the regions of caries 162 is helpful for assessing the condition of the extracted lesions and for monitoring the development of the identified lesions over time. The condition of caries in a tooth image can be quantified in a number of ways, including calculating the size (or area) of the lesion area and calculating fluorescence loss ratio of the lesion area.

In one example, the lesion area is calculated by counting the actual pixel number within the regions of caries 162, and then converting that to actual spatial dimension, such as mm².

In another example, the fluorescence loss is used to measure the condition of the caries. Fluorescence loss in tooth structure has been demonstrated to be a direct indication of the degree of demineralization in the structure. This quantity can be directly calculated from the intensity values in the tooth's fluorescence image. In the fluorescence image, the fluorescence loss ratio ΔF at each pixel within the lesion area is calculated using the formula below:

${{\Delta \; F} = \frac{I_{r} - I_{o}}{I_{r}}},$

where I_(r) is the reconstructed intensity value from step 140, and I_(o) is the actual measured intensity value of the green channel of the fluorescence image I_(Fluo). Where caries has occurred, ΔF>0.

The whole fluorescence loss L of the lesion region is the sum of ΔF within the lesion region R:

$L = {\sum\limits_{i \in R}\; {\Delta \; F_{i}}}$

FIG. 6A shows another method for quantification of caries using step 120, which relies on the method for locating tooth regions according to the present invention. This method for quantification of caries comprises a step of generating a FIRE image or other images obtained by combining a fluorescence image and a reflectance image of the tooth according to the present invention. FIG. 6A is similar to FIG. 1. However, in FIG. 6A the digital image of the tooth is a FIRE image or the like which is generated from both a reflectance image and a fluorescence image. Particularly, the reflectance image is generated using white or single color light, while the fluorescence image is generated under excitation light in the ultraviolet-blue range. During step 130 of identifying a sound region adjacent to the extracted lesion area, the fluorescence image may substitute the FIRE image as input, indicated by the dashed arrow 160 a. During step 140 of reconstructing intensity values within a lesion area and step 150 of quantifying the condition of the caries areas, the fluorescence image is also needed as input, indicated by the arrow 160.

FIG. 6B shows yet another method for quantifying caries using step 120, which relies on the method for locating tooth regions according to the present invention. It is similar to FIG. 6A, but differs in step 120 which specifically comprises steps of identifying the tooth regions 165 from a tooth image, extracting a suspicious lesion area, and removing false positives. The dashed arrow 160 a shows that the fluorescence image may be used for step 130, and the arrow 160 shows that the fluorescence image is used for steps 140 and 150.

Another alternative method for quantifying caries using step 120 relies on the method for locating interproximal tooth regions according to the present invention. Referring back to FIG. 1, the digital image generated in step 110 is a fluorescence image of the tooth. As discussed previously, the fluorescence image has similar characteristics as the FIRE image, and so the methods used in the lesion areas extraction step 120 can all be carried out on the fluorescence image. Therefore, in this alternative embodiment, the fluorescence image is used in all steps from Step 110 to Step 150.

The invention has been described in detail with particular reference to a presently preferred embodiment, but it will be understood that variations and modifications can be effected within the spirit and scope of the invention. The presently disclosed embodiments are therefore considered in all respects to be illustrative and not restrictive. The scope of the invention is indicated by the appended claims, and all changes that come within the meaning and range of equivalents thereof are intended to be embraced therein.

PARTS LIST

-   110 step of generating a digital image of a tooth -   120 step of extracting a lesion area from sound tooth regions -   130 step of identifying a sound region adjacent to the extracted     lesion area -   140 step of reconstructing intensity values for tooth tissue within     the lesion area -   150 step of calculating the condition of the caries -   160, 160 a arrow -   161 region of interest -   162 lesion area (or region of caries) -   163 segmentation border -   164 sound tooth area -   165 tooth region -   165 a, 165 b, 165 c tooth -   166 dilated line -   167 reflectance image -   168 fluorescence image -   169 FIRE image -   170 water basin corresponding to the circular internal marker area     222 -   172 a, 172 b, 172 c, 172 d water basins -   173 a, 173 b watershed lines -   174 a region corresponding to water basin 170 -   174 b region corresponding to a combination of water basins 172 a     and 172 b -   174 c region corresponding to a combination of water basins 172 c     and 172 d -   176 interlines between teeth -   180 processing apparatus -   200 origin point -   202 contour line -   210 ray lines -   220 a, 220 b, 220 c, 220 d gray areas corresponding to internal     markers -   222 circular area on the target tooth corresponding to internal     markers -   224 light areas corresponding to an external marker -   230 a, 230 b, 230 c seeded areas -   232 a, 232 b external markers -   234 a, 234 b, 234 c seed points -   236 a, 236 b, 236 c internal markers -   238 a, 238 b, 238 c water basin regions -   240 a, 240 b interlines 

1. A method for identifying one or more tooth regions, comprising: generating a first threshold image from a first image of the tooth by selecting intensity data values higher than a first predetermined threshold value c1; generating a second threshold image from a second image of the tooth by selecting intensity data values higher than a second predetermined threshold value c2; generating a preliminary tooth regions image that defines at least a first tooth region from the intersection of the first and second threshold images; generating a reference binary image from the first image by selecting intensity data values higher than a third predetermined threshold value c3, wherein threshold value c3 exceeds threshold value c1; and generating a refined tooth regions image from at least the first tooth region in the preliminary tooth regions image, wherein the at least the first tooth region is connected to objects in the reference binary image.
 2. The method of claim 1, wherein the first image is a grayscale fluorescence image of the tooth.
 3. The method of claim 1, wherein the second image is a grayscale reflectance image of the tooth.
 4. The method of claim 1, wherein one or both of the first and second images of the tooth are obtained from the green channels of the fluorescence image and reflectance image, respectively.
 5. The method of claim 1 further comprising identifying the one or more tooth regions using thresholding.
 6. The method of claim 1 further comprising processing the refined tooth regions image and generating a processed refined tooth regions image.
 7. The method of claim 6, wherein generating the processed refined tooth regions image includes removing small areas and filling holes resulting from the thresholding operation.
 8. The method of claim 1 further comprising displaying the refined tooth regions image.
 9. A method for identifying one or more tooth regions, comprising: accessing a fluorescence image of the tooth; accessing a reflectance image of the tooth; combining image data for the fluorescence and reflectance images to obtain a combined image of the tooth, generating a complement image from a grayscale combined image of the tooth; generating an intermediate image by applying a morphological reconstruction operation to the complement image; generating a first threshold image by applying a thresholding operation to the intermediate image with a first upper threshold value and generating a first processed threshold image from the first threshold image; generating a second threshold image by applying the thresholding operation to the intermediate image with a second upper threshold value and generating a second processed threshold image from the second threshold image, the second upper threshold value being larger than the first upper threshold value; and identifying the one or more tooth regions using the first and second processed threshold images.
 10. The method of claim 9, wherein identifying the one or more tooth regions using the first and second processed threshold images comprises: determining internal markers from the first processed threshold image; determining external markers for teeth from a complement of a dilated image of the second processed threshold image; calculating one or more Sobel gradient images from the grayscale channels of the combined image and the reflectance image; and applying the marker-controlled watershed transform to the summation of the one or more calculated Sobel gradient images using the internal markers and external markers to obtain the tooth regions.
 11. The method of claim 9, wherein identifying the one or more tooth regions using the first and second processed threshold images comprises: generating a dilated image from a complement image of the second processed threshold image in combination with data from the first processed threshold image; and identifying the one or more tooth regions from a complement image of the dilated image.
 12. The method of claim 9, wherein the grayscale combined image is obtained from the green channels of the fluorescence image and reflectance image.
 13. The method of claim 9, wherein the combined image is a FIRE image.
 14. The method of claim 9, wherein generating the complement image from the grayscale combined image of the tooth includes calculating the difference between the maximum value of the grayscale combined image and the grayscale combined image.
 15. The method of claim 9, wherein generating the first processed threshold image comprises removing the regions of small areas in the first threshold image and filling holes resulting from the thresholding operation.
 16. The method of claim 9, wherein generating the second processed threshold image comprises removing the regions of small areas in the second threshold image and filling holes resulting from the thresholding operation.
 17. The method of claim 9, wherein determining the internal markers comprises eroding the processed threshold image with a disk shaped structure element
 18. The method of claim 9, wherein the dilated image is obtained by using a disk shaped structure element.
 19. The method of claim 11, wherein generating the dilated image is performed by iteratively using a structure element of all 1's. 